- I work with animal models of breast cancer and conducted an experiment where I treated tumor-bearing mice with the CDK4/6 inhibitor, palbociclib.
- The dose-limiting toxicity of palbociclib is decreased white blood cell counts, so part of my experiment involved measuring the complete blood count and leukocyte differential of mice after treatment.
12/10/2019
Introduction and experimental design
Project setup
- First I loaded libraries, functions, and set my working directory.
library(tidyverse)
library(rio)
library(broom)
library(visdat)
library(plotly)
setwd("~/Downloads/Gallanis_BIOF339_final")
source("gregtheme.R") # a custom ggplot theme to match my other plots
- I integrated my R project files into my animal experiment folder for good housekeeping. (For this presentation all the files load from the downloads folder.)
Data import
- Then, I imported the data, which I had previously transcribed into Excel.
CBC_raw <- import("Mouse_CBCs.csv")
trt <- import("Exp_2_groups.csv")
str(CBC_raw)
## 'data.frame': 22 obs. of 19 variables: ## $ No : chr "Reference_Low" "Reference_High" "2200" "2193" ... ## $ WBC_(10^9/L) : num 6 15 3.38 2.5 2.03 2.84 4.37 1.89 3.44 3.92 ... ## $ LYM_(10^9/L) : num 3.4 7.44 2.5 1.76 1.47 2.24 2.87 1.54 2.71 3.28 ... ## $ MON_(10^9/L) : num 0 0.6 0.09 0.22 0.06 0.1 0.25 0.04 0.04 0.12 ... ## $ NEU_(10^9/L) : num 0.5 3.8 0.8 0.51 0.5 0.5 1.25 0.31 0.69 0.52 ... ## $ LY_% : num 57 93 73.8 70.6 72.4 78.7 65.7 81.5 78.8 83.6 ... ## $ MO_% : num 0 7 2.7 8.8 3 3.6 5.7 2.2 1.2 3.2 ... ## $ NE_% : num 8 48 23.5 20.5 24.6 17.7 28.6 16.3 20 13.2 ... ## $ RBC_(10^12/L): num 7 12 7.17 6.74 7.56 7.67 7.75 8.49 7.82 7.04 ... ## $ HGB_g/dL : num 12.2 16.2 11.4 10.8 11.9 11.9 11.3 13.5 13.1 10.6 ... ## $ HCT_% : num 35 45 37.6 39 39.8 ... ## $ MCV_fL : int 45 55 53 58 53 53 50 55 53 60 ... ## $ MCH_pg : num 11.1 12.7 16 16 15.8 15.5 14.6 15.9 16.7 15 ... ## $ MCHC_g/dL : num 22.3 32 30.4 27.7 29.9 28.9 29.2 29 31.4 25.1 ... ## $ RDWc_% : num NA NA 19.5 21.5 20.2 20.2 18.9 20.8 20.2 18.2 ... ## $ PLT_(10^9/L) : int 200 450 424 474 483 392 403 389 561 480 ... ## $ PCT_% : num NA NA 0.24 0.32 0.28 0.25 0.23 0.24 0.34 0.29 ... ## $ MPV_fL : num NA NA 5.8 6.7 5.8 6.4 5.7 6.2 6 6 ... ## $ PDWc_% : num NA NA 28.5 30.2 29.1 30.2 29.6 29.1 29.1 29.6 ...
str(trt)
## 'data.frame': 20 obs. of 3 variables: ## $ No : int 2193 2301 2325 2316 2320 2194 2200 2304 2326 2317 ... ## $ Group : chr "A" "A" "A" "A" ... ## $ Treatment: chr "Palbociclib" "Palbociclib" "Palbociclib" "Palbociclib" ...
Data reformatting and joining
- I adjusted the data formats, removed extraneous information such as the reference ranges, and merged the measurements with the treatment group annotations.
trt <- trt %>% mutate( No = as.character(No), Group = as.factor(Group), Treatment = as.factor(Treatment)) CBC <- CBC_raw %>% slice(-c(1,2)) %>% full_join(trt, by = "No") %>% arrange(No) CBC <- CBC[,c(1,21,20,2:19)]
str(CBC)
## 'data.frame': 20 obs. of 21 variables: ## $ No : chr "2193" "2194" "2195" "2200" ... ## $ Treatment : Factor w/ 2 levels "Palbociclib",..: 1 2 1 2 1 2 1 2 1 2 ... ## $ Group : Factor w/ 2 levels "A","B": 1 1 2 1 1 2 2 1 2 2 ... ## $ WBC_(10^9/L) : num 2.5 2.03 1.46 3.38 2.84 2.25 2.89 4.37 2.12 2.97 ... ## $ LYM_(10^9/L) : num 1.76 1.47 1.16 2.5 2.24 1.8 2.36 2.87 1.52 2.26 ... ## $ MON_(10^9/L) : num 0.22 0.06 0.07 0.09 0.1 0.07 0.08 0.25 0.1 0.07 ... ## $ NEU_(10^9/L) : num 0.51 0.5 0.22 0.8 0.5 0.38 0.45 1.25 0.51 0.63 ... ## $ LY_% : num 70.6 72.4 79.9 73.8 78.7 79.9 81.7 65.7 71.4 76.2 ... ## $ MO_% : num 8.8 3 4.8 2.7 3.6 3.3 2.7 5.7 4.7 2.4 ... ## $ NE_% : num 20.5 24.6 15.4 23.5 17.7 16.8 15.6 28.6 24 21.4 ... ## $ RBC_(10^12/L): num 6.74 7.56 6.9 7.17 7.67 5.48 6.92 7.75 4.61 7.56 ... ## $ HGB_g/dL : num 10.8 11.9 10.4 11.4 11.9 8.2 10.2 11.3 7 11.8 ... ## $ HCT_% : num 39 39.8 38.6 37.6 41 ... ## $ MCV_fL : int 58 53 56 53 53 58 57 50 55 58 ... ## $ MCH_pg : num 16 15.8 15.1 16 15.5 14.9 14.7 14.6 15.2 15.7 ... ## $ MCHC_g/dL : num 27.7 29.9 27 30.4 28.9 25.9 26 29.2 27.6 26.9 ... ## $ RDWc_% : num 21.5 20.2 19.5 19.5 20.2 17.4 17.7 18.9 21 17.4 ... ## $ PLT_(10^9/L) : int 474 483 367 424 392 538 398 403 325 563 ... ## $ PCT_% : num 0.32 0.28 0.23 0.24 0.25 0.41 0.24 0.23 0.2 0.36 ... ## $ MPV_fL : num 6.7 5.8 6.1 5.8 6.4 7.7 6.1 5.7 6.3 6.3 ... ## $ PDWc_% : num 30.2 29.1 29.1 28.5 30.2 31.2 30.7 29.6 30.2 29.6 ...
vis_dat(CBC_raw) vis_dat(CBC)
Left: Before processing. Right = After processing and joining
Taking a preliminary look
- First I sought to take a look at the plot of all the data
liveplot <- CBC %>% gather(Measurement, Value, names(select_if(.,is.numeric))) %>%
ggplot() +
geom_jitter(aes(x = Treatment, y = Value, color = Treatment),width = 0.2, height = 0.1) +
facet_wrap(~ Measurement, scales = "free") +
xlab("Treatment") +
ylab("Values (mean ± SD)") +
ggtitle("Mouse CBC values by treatment")
ggplotly(liveplot, tooltip = c("x","y","label"))
Preliminary look
Summary statistics and analyses
- Next, I calculated mean and standard deviation of the measurements for each CBC parameter, grouped by treatment.
- I also performed t tests to compare the means from palbocicilb-treated samples to vehicle-treated samples for each CBC parameter.
CBC_summary <- CBC %>%
gather(Measurement, Value, names(select_if(.,is.numeric))) %>%
group_by(Treatment, Measurement) %>%
summarize_at("Value",list(mean = mean, sd = sd)) %>%
arrange(Measurement)
CBC_ttest <- CBC %>%
select_if(is.numeric) %>%
sapply(function(i) t.test(i ~ CBC$Treatment, alternative = "two.sided")$p.value) %>%
tidy %>%
data.frame %>%
rename(Measurement = names, two_sided_p_val = x)
head(CBC_summary)
## # A tibble: 6 x 4 ## # Groups: Treatment [2] ## Treatment Measurement mean sd ## <fct> <chr> <dbl> <dbl> ## 1 Palbociclib HCT_% 38.4 7.13 ## 2 Vehicle HCT_% 40.2 3.80 ## 3 Palbociclib HGB_g/dL 10.5 1.97 ## 4 Vehicle HGB_g/dL 11.2 1.27 ## 5 Palbociclib LY_% 77.5 5.95 ## 6 Vehicle LY_% 75.4 4.26
CBC_ttest
## Measurement two_sided_p_val ## 1 WBC_(10^9/L) 0.428670423 ## 2 LYM_(10^9/L) 0.438061167 ## 3 MON_(10^9/L) 0.743560862 ## 4 NEU_(10^9/L) 0.405668715 ## 5 LY_% 0.379631407 ## 6 MO_% 0.118302441 ## 7 NE_% 0.128814347 ## 8 RBC_(10^12/L) 0.256954686 ## 9 HGB_g/dL 0.326749280 ## 10 HCT_% 0.490640877 ## 11 MCV_fL 0.304545869 ## 12 MCH_pg 0.703939032 ## 13 MCHC_g/dL 0.491687024 ## 14 RDWc_% 0.085109848 ## 15 PLT_(10^9/L) 0.001837129 ## 16 PCT_% 0.008826005 ## 17 MPV_fL 0.520929762 ## 18 PDWc_% 0.113294160
Adding annotations based on t test results
- I created a small data frame containing the annotations and coordinates for drawing comparison lines
annotations <- data.frame(Measurement = c("PLT_(10^9/L)","PCT_%"),
x1 = c(1,1), x2 = c(2,2),
y1 = c(625,0.6), y2 = c(650,0.64),
xlab = c(1.5,1.5), ylab = c(670,0.67),
lab = c("**","**"))
## Measurement x1 x2 y1 y2 xlab ylab lab ## 1 PLT_(10^9/L) 1 2 625.0 650.00 1.5 670.00 ** ## 2 PCT_% 1 2 0.6 0.64 1.5 0.67 **
Putting it all together
finalplot <- CBC %>% gather(Measurement, Value, names(select_if(.,is.numeric))) %>%
ggplot() +
geom_jitter(aes(x = Treatment, y = Value, color = Treatment),width = 0.2, height = 0.1) +
geom_errorbar(aes(x = Treatment, ymax = mean+sd, ymin = mean-sd, color = Treatment),
width = 0.1, data = CBC_summary, inherit.aes = FALSE, show.legend = FALSE) +
geom_errorbar(aes(x = Treatment, ymax = mean, ymin = mean, color = Treatment),
width = 0.2, data = CBC_summary, inherit.aes = FALSE, show.legend = FALSE) +
geom_text(data = annotations, aes(x = xlab, y = ylab, label = lab)) +
geom_segment(data = annotations, aes(x = x1, xend = x1, y = y1, yend = y2)) +
geom_segment(data = annotations, aes(x = x2, xend = x2, y = y1, yend = y2)) +
geom_segment(data = annotations, aes(x = x1, xend = x2, y = y2, yend = y2)) +
scale_color_manual(values=c("#EB4478","#000000")) +
scale_y_continuous(expand = c(.1,.1)) +
facet_wrap(~ Measurement, scales = "free") +
xlab("Treatment") +
ylab("Values (mean ± SD)") +
ggtitle("Mouse CBC values by treatment") +
gregtheme()
finalplot
Putting it all together
Conclusions
- Platelet count (concentration in blood) and plateletcrit (percent of blood volume occupied by platelets) were significantly decreased in the palbociclib-treated mice compared to the vehicle-treated mice.
- This is an on-target effect
- All other parameters were not significantly affected, although there was a trending decrease in neutrophil count in the palbociclib-treated group